Dr Sergey A. Karabasov

New PhD studentship and PostDoctoral opportunities!

PhD Studentship supported by China Scholarship Council (CSC) award in Queen Mary University of London is available to work on the project
"Multi-scale modelling of hydrodynamics-based focusing of protein evolution".
Outstanding candidates who are eligible for the CSC stipend are encouraged to contact me by email for further details

Introduction

Sergey Karabasov is a Visitor
in Cambridge University Engineering
Department in the Turbomachinery Group.

Research Interests

As the number of flights continues to increase each year, to reduce noise for those who live in the vicinity of the airports, each individual aircraft needs to be made quieter, and for turbofan engines jet noise remains a considerable noise source. In the past, most of the jet noise reduction for civil aircraft came from increasing the size of jet engine. This is because jet engine increase allowed reducing the jet speed for the same amount of thrust and, since jet mixing noise scales as a high power of the jet exit velocity, the noise was reduced too. However, any further decrease in noise is only possible if detailed noise mechanisms are quantified. In addition to jet mixing noise, for large size jets with a typical jet-under-wing configuration, effects of jet interaction with the wing and the airframe, such as jet-flap-interaction, need to be considered. This research aims to develop new computational modelling techniques for detailed investigation of effective jet noise sources based on a combination of computational methods and modern analytical techniques.
Collaborators: Ohio Aerospace Institute and NASA Glen US, Acoustic Division of Central Aerohydrodynamic Institute (TsAGI) Russia, University of Cambridge Department of Engineering UK.
Research Staff: Dr Vasily Semiltov
PhD Student: To Be Appointed
Funding: EPSRC, AARC, Royal Society of London, TsAGI

Modelling of broad band noise in water systems
Fluid generated broadband noise has been a challenging problem for the Aerospace industry over the past decades. The techniques developed and used within the Aerospace industry are currently being applied in water within BAE Systems which sponsors the current CASE PhD project. The aim of this PhD project is to address the prediction of broadband noise within very low speed water through the use of high fidelity CFD to better understand the turbulence physics and use this knowledge to better inform and refine lower cost stochastic noise prediction techniques.
Collaborators: BAE Systems
PhD Student: Stanislav Proskurin
Funding: EPSRC, BAE Systems

Computational modelling of large-scale geophysical flows in application to tidal turbine energy
Large-scale, interannual and decadal variability of the midlatitude ocean is a significant aspect of the global climate variability. Through their nonlinear interactions, mesoscale (10-100 km) eddies can not only maintain the mean circulation but also drive the observed variability. Computational modelling plays a big role in revealing new mechanisms of this variability and is ultimately important for understanding and prediction of the climate change. In addition to the fundamental questions of geophysical modelling, the modelling of large-scale effects, which drive the open-sea unsteadiness, is important for tidal turbine design since it determines the operating conditions for the turbine. This research aims to develop high-resolution computational techniques for ocean dynamics at meso-scale level and utilising these techniques, as well as the existing operational tidal models, to develop a hierarchy of nested boundary conditions for marine turbine simulations.
Collaborators: Department of Applied Mathematics Imperial College London, National Oceanography Centre Liverpool, University of Cambridge Department of Engineering
PhD Student: Robin Winkel
Funding: NERC

New computational algorithms for high-resolution turbulent flow simulations
General-purpose computational approaches are very important in turbulent flow modelling. Large Eddy Simulation (LES) is one popular approach of this type which is becoming increasingly popular due to increase in the computer power. LES methods rely on directly resolving large flow scale above a certain limit to capture all dynamically important flow scales. For achieving this under the constraint of a finite grid resolution, the use of high-resolution numerical methods is crucial. Based on the high-resolution schemes developed for nonlinear hyperbolic conservation laws in the group over the years, this research aims to extend the modelling to include: high-performance computing with efficient access to cache memory (CPU and GPU), asynchronous time stepping and nested grids, and multi-phase flow applications.
Collaborators: Moscow Institute of Nuclear Safety Russia, Perm State Polytechnic University Russia, Argonne National Laboratory US.
Research Staff: Dr Vasily Semiltov, Dr Anton Markesteijn
Funding: Royal Society of London, JSC Aviadvigatel Russia

Bridging molecular dynamics and continuum fluid mechanics in application to the modelling of complex molecular systems interaction in aqueous solutions
Interaction of large molecular systems dissolved in water is very important for applications which range from new drug design and bio-medical flows to chemical engineering. Fully atomistic simulations of large atomistic systems using pure molecular dynamics remain prohibitively expensive. This is largely because 90% of the resources are spent for the calculation of surrounding water molecules that are important for the molecular system transitions. Yet, the bulk of surrounding water does not directly contribute to the chemical reactions and could be simulated more efficiently through a collective continuum fluid dynamics contribution. This research aims to develop a new computational continuum- atomistic framework for acceleration of molecular dynamics simulations. In the framework, the continuum model based on stochastic fluctuating hydrodynamic equations is used as a boundary condition for fully atomistic simulations.
Collaborators: Aston University UK, Lomonosov Moscow State University Russia, RIKEN Institute Japan.
Research Staff: Anton Markesteijn
PhD Student: Pardis Tabaee
Funding: EPSRC, Royal Society of London